1. Technical Field
The present invention relates in general to process control systems. In particular, the present invention relates to process control optimization systems which utilize an adaptive optimization software system. In yet further particularity, the present invention relates to adaptive optimization software systems which comprise intelligent software objects (hereinafter xe2x80x9cISOxe2x80x9d) arranged in a hierarchical relationship whereby the goal seeking behavior of each ISO can be modified by ISOs higher in the ISO""s hierarchical structure. In yet further particularity, the present invention relates to ISOs comprising internal software objects including expert system objects, adaptive models objects, optimizer objects, predictor objects, sensor objects, and communication translation objects. In yet a further point of particularity, the present invention also relates to a method of human interaction with said adaptive optimization software system.
The present invention further relates to oilfield hydrocarbon production management systems capable of managing hydrocarbon production from boreholes. The present invention""s intelligent optimization oilfield hydrocarbon production management systems sense and adapt to internal and external process conditions, automatically adjusting operating parameters to optimize production from the wellbore with a minimum of human intervention. Oilfield hydrocarbon production management may be accomplished by systems located downhole, at the surface, subsea, or from a combination of these locations. The present invention""s oilfield hydrocarbon production management systems include one or more of the following features: intelligent and non-intelligent well devices such as flow control tools, smart pumps, and sensors; knowledge databases comprising historical databases, reservoir models, and wellbore requirements; and supervisory control and data acquisition software comprising one or more oilfield hydrocarbon production management goals, one or more process models, and, optionally, one or more goal seeking intelligent software objects.
2. Background Art
Process control systems are used in a variety of applications to sense process conditions and adjust process operating parameters in an attempt to optimize performance for given sets of goals. Many current conventional process control systems use static representations of the process to be controlled and do not provide for changes in the process control model being used in real time. In conventional adaptive control theory, a suitable controller structure is chosen and the parameters of the controller are adjusted using static rules so that the output of the process follows the output of the reference of the model asymptotically. Static rules do not permit a process control system to automatically and optimally adapt to changing process conditions. One significant deficiency of prior art process control systems, whether or not adaptive, is their lack of an intuitive user interface, either for initially configuring a system or for interacting with the system in real-time.
Another significant deficiency of prior art process control systems, whether or not adaptive, is the inability of the process control system to automatically perform control actions and, in so doing, provide a global goal-seeking mechanism that ties the process control system together into a powerful unified system to achieve the highest optimization congruent with management objectives and goals.
Further, many process control systems in the prior art provide for limited levels of control point, component, and/or system modeling or control hierarchies.
Accordingly, many prior art process control systems, whether or not adaptive, cannot provide concurrent multi-level optimization ranging from specific, component-oriented, narrowly focused levels to the broadest, global level.
Traditional process control systems are built up of discrete components (i. e., sensors and controllers) that work independently and lack low-level optimization. Some systems optimize on a global, system level without regard to optimization at each component level, while still other systems optimize only at the component level. As no global goal-seeking mechanism ties the parts together into a powerful unified system to achieve management objectives, the overall process fails to achieve its highest optimization and integration of low-level or component level optimization with the higher level or system level optimization.
Many systems that do provide some amount of concurrent multi-level optimization rely on just one or two methods of achieving the desired concurrent multi-level optimization, rather than on a multiplicity and variety of methods including expert systems, adaptive models which can use one or more modeling methodologies including neural networks, and other predictive modeling techniques.
Among the limited numbers of systems that use a variety of methods, no process control system uses interacting, differing adaptive methods to dynamically change its chosen predictive models in real-time without having to stop either the process being controlled or the process control system.
Moreover, many current conventional process control systems rely upon human operators to determine and implement optimum set points throughout the domain of the process control system in real-time. These process control systems require human intervention to optimize processes and systems, but because human operators vary greatly as to experience and the soundness of their control reasoning, this human factor introduces a wide-ranging variable in the overall effectiveness of the process control system.
Expert systems have provided a significant improvement over traditional process control systems that do not use expert systems. However, many current art process control systems do not use expert systems to assist in adaptation of process control algorithm operation, algorithm selection, or algorithm parameter estimation.
Further, once installed, current art process control systems that do use expert systems lack automatic, systematic approaches to adaptively optimizing its expert system and the expert system""s algorithms.
Neural networks are a powerful modeling technique used to assure that the process model accurately predicts the performance of the modeled process over time. However, neural networks have a well known problem of xe2x80x9cmemorizingxe2x80x9d and thereby becoming xe2x80x9cstaticxe2x80x9d and unable to find mutated, differing models to more accurately predict process performance over time.
Further, neural networks by definition depend on the user""s omniscience to function correctly, and as user omniscience cannot be guaranteed, neural networks based systems suffer from reliance on user omniscience.
Moreover, some neural networks require weights used for the neural network""s evaluation to be derived from both the constraints to be implemented and from any data functions necessary for solution; these may not be available as inputs to the network, thus limiting the neural networks"" applicability to the process control system due to the inability to learn how to calculate these weights in real-time.
In the current art, production management of hydrocarbons from wells is highly dependent on human operators. However, operation of these wells has become more complex, giving rise to the need for more complex controls, including concurrent controlling of zone production, isolating specific zones, monitoring each zone in a particular well, monitoring zones and wells in a field, and optimizing the operation of wells in real-time across a vast number of optimization criteria. This complexity has placed production management beyond the control of one or even a few humans and necessitates at least some measure of automated controls.
Some current art oilfield hydrocarbon production management systems use computerized controllers to control downhole devices such as hydro-mechanical safety valves. These typically microprocessor-based controllers may also be used for zone control within a well. However, these controllers often fail to achieve the desired production optimization and further require substantial human intervention.
Additionally, current art oilfield hydrocarbon production management systems may use surface controllers that are often hardwired to downhole sensors which transmit data about conditions such as pressure, temperature, and flow to the surface controller. These data may then be processed by a computerized control system at the surface, but such systems still require human intervention and do not provide enforcement of global optimization criteria, focusing instead, if at all, on highly localized optimization, e.g., for one device.
Some current art oilfield hydrocarbon production management systems also disclose downhole intelligent devices, mostly microprocessor-based, including microprocessor-based electromechanical control devices and sensors, but do not teach that these downhole intelligent devices may themselves automatically initiate the control of electromechanical devices based on adaptive process models. Instead, these systems also require control electronics located at the surface as well as human intervention.
Accordingly, current oilfield hydrocarbon production management systems generally require a surface platform associated with each well for supporting the control electronics and associated equipment. In many instances, the well operator would rather forego building and maintaining a costly platform.
None of the current art disclosing intelligent downhole devices for controlling the production from oil and gas wells teaches the use of electronic controllers, electromechanical control devices and sensorsxe2x80x94whether located downhole, surface, subsea, or mixedxe2x80x94together with supervisory control and data acquisition (SCADA) systems which automatically adapt operation of the electronic controllers, electro-mechanically controllable devices, and/or sensors in accordance with process models and production management goals, or cooperative control of these devices based on a unified, adaptively optimizing system to automatically enforce system wide set of optimization criteria.
It is therefore an objective of the present invention to provide a process control optimization system that uses dynamic representations of the process to be controlled, thus providing for changes in the process control model being used in real time.
It is a further objective of the present invention to provide a process control optimization. system that automatically and optimally adapts to changing process conditions.
It is a further objective of the present invention to provide a process control optimization system having an intuitive user interface for both initially configuring a system and for interacting with the system in real-time.
It is a further objective of the present invention to provide a process control optimization system having a global goal-seeking mechanism that ties a process control system together into a powerful unified system that achieves the highest optimization congruent with management objectives and goals.
It is a further objective of the present invention to provide a process control optimization system having virtually unlimited levels of modeling and control hierarchy as well as virtually unlimited numbers of component-level process control points.
It is a further objective of the present invention to provide a process control optimization system having concurrent multi-level optimization ranging from specific, component-oriented, narrowly focused levels to the broadest, global level.
It is a further objective of the present invention to provide a process control optimization system that achieves the highest optimization and integration of low-level or component level optimization together with higher level or system level optimization.
It is a further objective of the present invention to provide a process control optimization system having said concurrent multi-level optimization using a variety of methods including expert systems, adaptive models which can use one or more modeling methodologies including neural networks, and other predictive modeling techniques.
If is a further objective of the present invention to allow a process control optimization system to use one or more interacting, differing adaptive methods to dynamically change its chosen predictive models in real-time without having to stop either the process being controlled or the process control system.
It is a further objective of the present invention to provide a process control optimization system that optimizes processes and systems consistent with management objectives without the need for continuing human intervention.
It is a further objective of the present invention to provide a process control optimization system that use expert systems to assist in adaptation of algorithm operation, algorithm selection, and algorithm parameter estimation.
It is a further objective of the present invention to provide a process control optimization system that automatically, systematically approaches adaptively optimizing its expert system and the expert system""s algorithms.
It is a further objective of the present invention to provide a process control optimization system that adaptively uses neural networks to prevent memorization by the neural networks. It is a further objective of the present invention to provide a process control optimization system that does not depend on a user""s omniscience to function correctly.
It is a further and final objective of the present invention to provide an improved automatic optimization oilfield hydrocarbon production management system.
As more fully described herein below, the present invention provides a process control optimization system that achieves, in substantial measure, these above stated objectives by including intelligent software objects (xe2x80x9cISOxe2x80x9d or xe2x80x9cISOsxe2x80x9d); an adaptive optimization software system comprising ISOs; a method of initializing said adaptive optimization software system; and a method of human interaction with said adaptive optimization software system. Accordingly, an improved automatic optimization oilfield hydrocarbon production management system is further described herein.